Impacts of High-Resolution Land and Ocean Surface Initialization on Local Model Predictions of Convection - PowerPoint PPT Presentation

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Impacts of High-Resolution Land and Ocean Surface Initialization on Local Model Predictions of Convection

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Title: Impacts of High-Resolution Land and Ocean Surface Initialization on Local Model Predictions of Convection


1
Impacts of High-Resolution Land and Ocean Surface
Initialization on Local Model Predictions of
Convection
Jonathan L. Case ENSCO, Inc./Short-term
Prediction Research and Transition (SPoRT)
Center Huntsville, Alabama
  • Talk Outline
  • Experiment objectives
  • NASA Data and Tools
  • Goddards Land Information System (LIS)
  • Moderate Resolution Imaging Spectroradiometer
    (MODIS)
  • Simulation methodology
  • Preliminary results
  • Future work

2
Hypothesis and Experiment Objectives
  • Hypothesis High-resolution land and water
    datasets from NASA utilities can lead to
    improvements in simulated summertimepulse-type
    convection over the S.E. U.S.
  • Experiment objectives
  • Use NASA LIS to provide high-resolution land
    surface initializations
  • Incorporate SPoRT MODIS composites for detailed
    representation of sea surface temperatures (SSTs)
  • Demonstrate proof of concept in using these
    datasets in local model applications with the
    Weather Research and Forecasting (WRF) model
  • Quantify possible improvements to WRF simulations

3
NASA Land Information System (LIS)
  • High-performance land surface modeling and data
    assimilation system
  • Runs a variety of Land Surface Models (LSMs)
  • Integrates satellite, ground, and reanalysis data
    to force LSMs in offline mode
  • Can run coupled to Advanced Research WRF
  • Data assimilation capability (EnKF) built-in
  • Modular framework enables easy substitution
    ofdatasets, LSMs, forcings, etc.
  • Adopted by AFWA for operational use in WRF
  • Previous SPoRT work with LIS
  • Case et al. (2008) manuscript in J. Hydrometeor.
  • Quantified positive impacts to WRF forecasts over
    Florida by initializing model with LIS land
    surface output
  • Focused on verification of primary meteorological
    variables

4
Land Surface Modeling with LIS
Inputs
Physics
Outputs
Applications
Topography, Soils
Land Surface Models (e.g. Noah, VIC, SIB, SHEELS)
Soil Moisture Temp
Weather/ Climate Water Resources Homeland Sec
urity Military Ops Natural Hazards
Land Cover, Vegetation Properties
Evaporation
Meteorology (Atmospheric Forcing)
Runoff
Data Assimilation Modules
Snow Soil Moisture Temperature
Snowpack Properties
5
MODIS SST Product
RTG
OSTIA
MODIS
Once daily 1/12 degree resolution
Once daily 5-km resolution
Four times daily 1-km resolution
  • MODIS provides superior resolution
  • Quality check with the latency product
  • Current weakness is high latency in areas with
    persistent cloud cover
  • Collaboration with Jet Propulsion Laboratory to
    improve product with AMSR-E data

MODIS
Latency Product
6
Experiment Design
  • Run parallel WRF simulations
  • Once daily 27-h simulations, initialized at 0300
    UTC
  • Control Initial / boundary conditions from NCEP
    12-km NAM model
  • Experimental Same as Control, except
  • Replace land surface data with LIS output fields
  • Replace SSTs with SPoRT MODIS composites
  • Evaluation and Verification
  • Graphical and subjective comparisons
  • Verification using Meteorological Evaluation
    Tools (MET) package
  • Developed by WRF Development Testbed Center
  • Standard point/grid verification statistics
  • Method for Object-Based Diagnostic Evaluation
    (MODE)
  • Object-oriented, non-traditional verification
    method
  • Summer convective precipitation verification

7
WRF Model Configuration
  • Model domain over Southeastern U.S.
  • Advanced Research WRF v3.0.1.1
  • 4-km horizontal grid spacing
  • 39 sigma-p levels from surface to 50 mb
  • Min. spacing near surface of 0.004 sigma
  • Max. spacing of 0.034 sigma
  • Positive definite advection of scalars
  • Model physics options
  • Radiation Dudhia SW and RRTM LW
  • Microphysics WSM6
  • Land Surface Noah LSM (same as LIS)
  • PBL MYJ scheme
  • Cumulus parameterization None

8
LIS Offline Spin-up Run
  • LIS/Noah LSM run from 1 Jan 2004 to 1 Sep 2008
  • Same soil and vegetation parameters as in WRF
  • Atmospheric forcing
  • 3-hourly Global Data Assimilation System analyses
  • Hourly Stage IV radar gauge precipitation
    products
  • Run long enough to allow soil to reach
    equilibrium state
  • Output GRIB-1 files to initialize WRF land
    surface variables
  • Incorporation of LIS data into WRF initial
    condition
  • Slight modifications to WRF Preprocessing System
    (WPS)
  • Created Vtable.LIS added LIS fields into
    METGRID.TBL file
  • Soil moisture/temperature, skin temp, snow-water,
    land-sea mask
  • LIS data over-write NAM land surface data
  • Similarly, MODIS SSTs over-write NAM / RTG SSTs
    in WPS

9
Precip Verification with MET / MODE
  • Traditional grid point verification
  • Bias, threat score, HSS at various accumulation
    intervals / thresholds
  • Neighborhood precipitation verification
  • Occurrence of precipitation threshold in a box
    surrounding a grid point
  • Relaxes stringency and determines model skill at
    distance thresholds
  • MODE object classification
  • Resolves objects through convolution
    thresholding
  • Filter function applied to raw data using a
    tunable radius of influence
  • Filtered field thresholded (tunable parameter) to
    create mask field
  • Raw data restored to objects where mask
    meets/exceeds threshold
  • For our study, MODE is run with
  • 1-h, 3-h accumulated precipitation
  • 5 mm, 10 mm, and 25 mm thresholds
  • Radius of influence 12 km (produced best object
    matching)

10
1 June 2008 Sensitivity Example0-10 cm Soil
Moisture Differences
11
1 June 2008 Sensitivity ExampleSST Differences
12
1 June 2008 Sensitivity ExampleWRF 3-h Precip
Diffs (06z to 06z)
Control
LIS
LIS CNTL
Stage IV
13
1 June 2008 Sensitivity ExampleMODE 5-mm / 3-h
precip Objects
Control Control LISMOD LISMOD
Fcst hour Grid Area Match Grid Area Un-match Grid Area Match Grid Area Un-match
3 0 1389 0 1389
6 0 169 0 206
9 0 1408 0 1421
12 0 2092 0 2066
15 54 1415 185 1351
18 3611 4415 3212 3864
21 5197 7602 6124 7034
24 1005 6362 1437 5868
27 1160 1013 102 2000
14
Precip Verification Jun-Aug 2008
  • WRF has high bias
  • LISMOD reduces bias some, esp. during
    daylight hours (12-24 h)
  • WRF generally has low skill (Heidke SS,
    right)
  • LISMOD incrementally improves skill, esp. at
    higher thresholds

15
Neighborhood Precipitation Verification
16
MODE Precip Object Verification
3-h Accumulated Precip Objects
1-h Accumulated Precip Objects
17
Summary / Future Work
  • Simulation methodology using NASA data and tools
  • Land Information System land surface data
  • MODIS SST composites
  • Provides high-resolution representation of
    land/water surface, consistent with local
    regional modeling applications
  • Ongoing / Future efforts
  • Conduct rigorous model verification
  • Use MET to generate objective statistics and
    object-oriented output for precipitation
  • Evaluate how combined NASA surface datasets can
    lead to improved short-term local model forecasts
    of convection
  • NASA / SPoRT website http//weather.msfc.nasa.gov
    /sport/

18
Backups
19
LIS High-Level Overview
Coupled or Forecast Mode
Uncoupled or Analysis Mode
WRF
Station Data
Global, Regional Forecasts and (Re-) Analyses
ESMF
LSM Physics (e.g. Noah, VIC, SIB,SHEELS)
Satellite Products
20
SPoRT MODIS SST Composites
  • Real-time, 1-km SST product
  • Composites available up to four times per day
  • 0400, 0700, 1600, and 1900 UTC
  • Primarily over Gulf of Mexico, western Atlantic
    waters, and larger lakes (e.g. Floridas Lake
    Okeechobee)
  • GRIB-1 files posted to publicly available ftp
    site
  • Sub-sampled to 2-km spacing for model
    applications
  • Compositing technique
  • Build complete SST composite with multiple Earth
    Observing System satellite passes (both Aqua and
    Terra)
  • At each pixel, examine 5 most recent readings
  • Take average of 3 warmest readings
  • This method helps to eliminate cloud contamination

21
Tropical Storm FayRainfall and Dramatic Soil
Moistening
22
Tropical Storm FayRainfall and Dramatic Soil
Moistening
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